Daily Human Activity Recognition Using Non-Intrusive Sensors

Abstract

In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

“…As a result, heterogeneous sensors have been used for AR tasks, which combine multimodal sensors (such as smartphones and wearable sensors) to improve AR performance [29][30][31]72,73], which is the case for our proposed scheme. With the evolvement of deep learning algorithms in recent years, some authors have made use of these algorithms for the automatic extraction of high-level features from the sensor data to achieve promising AR results [74][75][76][77][78]. The survey work in [79,80] investigated the latest trends in sensor-based AR studies based on deep learning models and explained their pros and cons along with the future recommendations/implications.…”

Improving Human Activity Recognition Integrating LSTM With Different Data Sources: Features, Object Detection and Skeleton Tracking

“…Some methods use various types of sensors while others use vision-based techniques [14]. In the first case, some works use sensors strategically placed in the house, as in [15], where a 95.42% success rate was obtained detecting 10 activities from the CASAS dataset [16]. Regarding on-body sensors, this process can use specific sensors or complete devices that include them, such as smartphones.…”

A Survey of Artificial-Intelligence Enabled Digital Transformation in Elderly Healthcare Field

“…For the activity recognition of the elderly, some researchers focused on easily recognized events [49][50][51], such as sleeping, going out, bathing, etc. Moreover, some researchers studied the recognition of complex daily activities of the elderly [52][53][54][55][56][57], especially their abnormal behavior. Also, researchers developed and designed systems and robots for the ambient assisted living services for the elderly, including pressure ulcers prevention system [58], robots for medicines identifying and daily physical activities planning [59,60], smart kitchen [61], and smart homes [62],…”